Namkung Junghyun
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
Methods Mol Biol. 2012;850:371-97. doi: 10.1007/978-1-61779-555-8_20.
Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, I review two popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE) and the mixed model with a polygenic random component. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. The analysis procedures are demonstrated using the ASSOC program in the S.A.G.E. package and the R package gee, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.
基于家系的关联分析在不考虑亲本基因型的情况下对观察到的基因型效应进行建模。这种方法已被证明比条件方法具有更强的效力。在本章中，我将回顾两种考虑家族相关性的流行关联分析方法：使用广义估计方程（GEE）的边际模型和具有多基因随机成分的混合模型。边际方法没有明确对家族相关性进行建模，而是利用这些信息来提高参数估计的效率。当相关结构不是研究重点时，使用GEE的这个模型很有用；相关性被视为干扰参数。在混合模型中，家族相关性被建模为随机效应，例如，多基因遗传模型考虑了源自家族内共享基因组成分的相关性。这些非条件方法为一般系谱数据提供了一个灵活的建模框架，以适应具有各种分布和多种协变量效应的性状。使用S.A.G.E.软件包中的ASSOC程序和R软件包gee演示了分析过程，包括如何准备输入数据、进行分析以及解释输出结果。ASSOC允许模型纳入可能无法被多基因效应充分解释的其他家族相关性的随机成分，并通过变换方法处理响应变量的非正态性。凭借其易用性，ASSOC为大型系谱数据的关联分析提供了一个有用的工具。
